{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Random NN modes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial shows how to build neural network models.\n",
    "We will learn how to calculate compositional descriptors using `xenonpy.descriptor.Compositions` calculator and train our model using `xenonpy.model.training` modules.\n",
    "\n",
    "In this tutorial, we will use some inorganic sample data from materials project. \n",
    "If you don't have it, please see https://github.com/yoshida-lab/XenonPy/blob/master/samples/build_sample_data.ipynb."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### useful functions\n",
    "\n",
    "Running the following cell will load some commonly used packages, such as `numpy`, `pandas`, and so on. It will also import some in-house functions used in this tutorial. See `samples/tools.ipynb` to check what will be imported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run tools.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sequential linear model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use `xenonpy.model.SequentialLinear` to build a sequential linear model. The basic layer in `SequentialLinear` model is `xenonpy.model.LinearLayer`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mSequentialLinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0min_features\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mout_features\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mbias\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mh_neurons\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mh_bias\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mh_dropouts\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mh_normalizers\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mh_activation_funcs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mCallable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mCallable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mReLU\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "Sequential model with linear layers and configurable other hype-parameters.\n",
       "e.g. ``dropout``, ``hidden layers``\n",
       "\u001b[0;31mInit docstring:\u001b[0m\n",
       "Parameters\n",
       "----------\n",
       "in_features\n",
       "    Size of input.\n",
       "out_features\n",
       "    Size of output.\n",
       "bias\n",
       "    Enable ``bias`` in input layer.\n",
       "h_neurons\n",
       "    Number of neurons in hidden layers.\n",
       "    Can be a tuple of floats. In that case,\n",
       "    all these numbers will be used to calculate the neuron numbers.\n",
       "    e.g. (0.5, 0.4, ...) will be expanded as (in_features * 0.5, in_features * 0.4, ...)\n",
       "h_bias\n",
       "    ``bias`` in hidden layers.\n",
       "h_dropouts\n",
       "    Probabilities of dropout in hidden layers.\n",
       "h_normalizers\n",
       "    Momentum of batched normalizers in hidden layers.\n",
       "h_activation_funcs\n",
       "    Activation functions in hidden layers.\n",
       "\u001b[0;31mFile:\u001b[0m           ~/projects/XenonPy/xenonpy/model/sequential.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xenonpy.model import SequentialLinear, LinearLayer\n",
    "SequentialLinear?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mLinearLayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0min_features\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mout_features\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mbias\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdropout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mactivation_func\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mCallable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mReLU\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnormalizer\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "Base NN layer. This is a wrap around PyTorch.\n",
       "See here for details: http://pytorch.org/docs/master/nn.html#\n",
       "\u001b[0;31mInit docstring:\u001b[0m\n",
       "Parameters\n",
       "----------\n",
       "in_features:\n",
       "    Size of each input sample.\n",
       "out_features:\n",
       "    Size of each output sample\n",
       "dropout: float\n",
       "    Probability of an element to be zeroed. Default: 0.5\n",
       "activation_func: func\n",
       "    Activation function.\n",
       "normalizer: func\n",
       "    Normalization layers\n",
       "\u001b[0;31mFile:\u001b[0m           ~/projects/XenonPy/xenonpy/model/sequential.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LinearLayer?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following the official suggestion from PyTorch, `SequentialLinear` is a python class that inherit the `torch.nn.Module` class. Users can specify the hyperparameters to get a fully customized model object. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SequentialLinear(\n",
       "  (layer_0): LinearLayer(\n",
       "    (linear): Linear(in_features=290, out_features=232, bias=True)\n",
       "    (dropout): Dropout(p=0.1)\n",
       "    (normalizer): BatchNorm1d(232, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (activation): ReLU()\n",
       "  )\n",
       "  (layer_1): LinearLayer(\n",
       "    (linear): Linear(in_features=232, out_features=203, bias=True)\n",
       "    (dropout): Dropout(p=0.1)\n",
       "    (normalizer): BatchNorm1d(203, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (activation): ReLU()\n",
       "  )\n",
       "  (layer_2): LinearLayer(\n",
       "    (linear): Linear(in_features=203, out_features=174, bias=True)\n",
       "    (dropout): Dropout(p=0.1)\n",
       "    (normalizer): BatchNorm1d(174, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (activation): ReLU()\n",
       "  )\n",
       "  (output): Linear(in_features=174, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SequentialLinear(290, 1, h_neurons=(0.8, 0.7, 0.6))\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### fully randomized model generation\n",
    "\n",
    "Process of random model generation:\n",
    "\n",
    "1. using a random parameter generator to generate a set of parameter.\n",
    "2. using the generated parameter set to setup a model object.\n",
    "3. loop step 1 and 2 as many times as needed.\n",
    "\n",
    "We provided a general parameter generator `xenonpy.utils.ParameterGenerator`  to do all the 3 steps for any callable object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mParameterGenerator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mseed\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCallable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDict\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m      Generator for parameter set generating.\n",
       "\u001b[0;31mInit docstring:\u001b[0m\n",
       "Parameters\n",
       "----------\n",
       "seed\n",
       "    Numpy random seed.\n",
       "kwargs\n",
       "    Parameter candidate.\n",
       "\u001b[0;31mFile:\u001b[0m           ~/projects/XenonPy/xenonpy/utils/parameter_gen.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xenonpy.utils import ParameterGenerator\n",
    "ParameterGenerator?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calling an instance of `ParameterGenerator` will return a generator.\n",
    "This generator can randomly select parameters from parameter candidates and yield them as a dict. This is what we want in step 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import ceil\n",
    "from random import uniform\n",
    "\n",
    "generator = ParameterGenerator(\n",
    "    in_features=290,\n",
    "    out_features=1,\n",
    "    h_neurons=dict(\n",
    "        data=[ceil(uniform(0.1, 1.2) * 290) for _ in range(100)], \n",
    "        repeat=(2, 3)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because `generator` is a generator, `for ... in ...` statement can be applied. \n",
    "For example, we can use `for parameters in generator(num_of_models)` to loop all these generated parameter sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'in_features': 290, 'out_features': 1, 'h_neurons': (70, 109)}\n",
      "{'in_features': 290, 'out_features': 1, 'h_neurons': (135, 45)}\n",
      "{'in_features': 290, 'out_features': 1, 'h_neurons': (216, 261, 79)}\n",
      "{'in_features': 290, 'out_features': 1, 'h_neurons': (111, 88, 49)}\n",
      "{'in_features': 290, 'out_features': 1, 'h_neurons': (216, 79, 47)}\n",
      "{'in_features': 290, 'out_features': 1, 'h_neurons': (161, 45)}\n",
      "{'in_features': 290, 'out_features': 1, 'h_neurons': (193, 161, 78)}\n",
      "{'in_features': 290, 'out_features': 1, 'h_neurons': (90, 36, 234)}\n",
      "{'in_features': 290, 'out_features': 1, 'h_neurons': (230, 83, 295)}\n",
      "{'in_features': 290, 'out_features': 1, 'h_neurons': (171, 247)}\n"
     ]
    }
   ],
   "source": [
    "for parameters in generator(num=10):\n",
    "    print(parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For step 2, we can give a model class to the `factory` parameter as a factory function.\n",
    "If `factory` parameter is given, generator will feed generated parameters to the factory function automatically and yield the result as a second return in each loop. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parameters:  {'in_features': 290, 'out_features': 1, 'h_neurons': (182, 335, 204)}\n",
      "SequentialLinear(\n",
      "  (layer_0): LinearLayer(\n",
      "    (linear): Linear(in_features=290, out_features=182, bias=True)\n",
      "    (dropout): Dropout(p=0.1)\n",
      "    (normalizer): BatchNorm1d(182, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (activation): ReLU()\n",
      "  )\n",
      "  (layer_1): LinearLayer(\n",
      "    (linear): Linear(in_features=182, out_features=335, bias=True)\n",
      "    (dropout): Dropout(p=0.1)\n",
      "    (normalizer): BatchNorm1d(335, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (activation): ReLU()\n",
      "  )\n",
      "  (layer_2): LinearLayer(\n",
      "    (linear): Linear(in_features=335, out_features=204, bias=True)\n",
      "    (dropout): Dropout(p=0.1)\n",
      "    (normalizer): BatchNorm1d(204, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (activation): ReLU()\n",
      "  )\n",
      "  (output): Linear(in_features=204, out_features=1, bias=True)\n",
      ") \n",
      "\n",
      "parameters:  {'in_features': 290, 'out_features': 1, 'h_neurons': (108, 255)}\n",
      "SequentialLinear(\n",
      "  (layer_0): LinearLayer(\n",
      "    (linear): Linear(in_features=290, out_features=108, bias=True)\n",
      "    (dropout): Dropout(p=0.1)\n",
      "    (normalizer): BatchNorm1d(108, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (activation): ReLU()\n",
      "  )\n",
      "  (layer_1): LinearLayer(\n",
      "    (linear): Linear(in_features=108, out_features=255, bias=True)\n",
      "    (dropout): Dropout(p=0.1)\n",
      "    (normalizer): BatchNorm1d(255, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (activation): ReLU()\n",
      "  )\n",
      "  (output): Linear(in_features=255, out_features=1, bias=True)\n",
      ") \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for parameters, model in generator(2, factory=SequentialLinear):\n",
    "    print('parameters: ', parameters)\n",
    "    print(model, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### scheduled random model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also enable users to generate parameters in a functional way by given a function as candidate parameters. In this case, the function accept an int number as vector length and a vector of generated parameters. For example, generate a `n` length vector with float numbers sorted in ascending."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = ParameterGenerator(\n",
    "    in_features=290,\n",
    "    out_features=1,\n",
    "    h_neurons=dict(\n",
    "        data=lambda n: sorted(np.random.uniform(0.2, 0.8, size=n), reverse=True), \n",
    "        repeat=(2, 3)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parameters:  {'in_features': 290, 'out_features': 1, 'h_neurons': (0.7954011465554711, 0.6993610045591458)}\n",
      "SequentialLinear(\n",
      "  (layer_0): LinearLayer(\n",
      "    (linear): Linear(in_features=290, out_features=231, bias=True)\n",
      "    (dropout): Dropout(p=0.1)\n",
      "    (normalizer): BatchNorm1d(231, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (activation): ReLU()\n",
      "  )\n",
      "  (layer_1): LinearLayer(\n",
      "    (linear): Linear(in_features=231, out_features=203, bias=True)\n",
      "    (dropout): Dropout(p=0.1)\n",
      "    (normalizer): BatchNorm1d(203, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (activation): ReLU()\n",
      "  )\n",
      "  (output): Linear(in_features=203, out_features=1, bias=True)\n",
      ") \n",
      "\n",
      "parameters:  {'in_features': 290, 'out_features': 1, 'h_neurons': (0.798810246990777, 0.38584535382368323, 0.29569841893337045)}\n",
      "SequentialLinear(\n",
      "  (layer_0): LinearLayer(\n",
      "    (linear): Linear(in_features=290, out_features=232, bias=True)\n",
      "    (dropout): Dropout(p=0.1)\n",
      "    (normalizer): BatchNorm1d(232, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (activation): ReLU()\n",
      "  )\n",
      "  (layer_1): LinearLayer(\n",
      "    (linear): Linear(in_features=232, out_features=112, bias=True)\n",
      "    (dropout): Dropout(p=0.1)\n",
      "    (normalizer): BatchNorm1d(112, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (activation): ReLU()\n",
      "  )\n",
      "  (layer_2): LinearLayer(\n",
      "    (linear): Linear(in_features=112, out_features=86, bias=True)\n",
      "    (dropout): Dropout(p=0.1)\n",
      "    (normalizer): BatchNorm1d(86, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (activation): ReLU()\n",
      "  )\n",
      "  (output): Linear(in_features=86, out_features=1, bias=True)\n",
      ") \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for parameters, model in generator(2, factory=SequentialLinear):\n",
    "    print('parameters: ', parameters)\n",
    "    print(model, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model training\n",
    "\n",
    "We provide a general and extendable model training system for neural network models.\n",
    "By customizing the extensions, users can fully control their model training process, and save the training results following the *XenonPy.MDL* format automatically.\n",
    "\n",
    "All these modules and extensions are under the `xenonpy.model.training`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "from pymatgen import Structure\n",
    "\n",
    "from xenonpy.model.training import Trainer, SGD, MSELoss, Adam, ReduceLROnPlateau, ExponentialLR, ClipValue\n",
    "\n",
    "from xenonpy.datatools import preset, Splitter\n",
    "from xenonpy.descriptor import Compositions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### prepare training/testing data\n",
    "\n",
    "If you followed the tutorial in https://github.com/yoshida-lab/XenonPy/blob/master/samples/build_sample_data.ipynb,\n",
    "the sample data should be save at `~/.xenonpy/userdata` with name `mp_samples.pd.xz`.\n",
    "You can use `pd.read_pickle` to load this file but we suggest that you use our `xenonpy.datatools.preset` module.\n",
    "\n",
    "We chose `volume` as the example to demostrate how to use our training system. We only use the data which `volume` are smaller than 2,500 to avoid the disparate data.\n",
    "we select data in `pandas.DataFrame` and calculate the descriptors using `xenonpy.descriptor.Compositions` calculator.\n",
    "It is noticed that the input for a neuron network model in pytorch must has shape (N x M x ...), which mean if the input is a 1-D vector, it should be reshaped to 2-D, e.g. (N) -> (N x 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>band_gap</th>\n",
       "      <th>composition</th>\n",
       "      <th>density</th>\n",
       "      <th>e_above_hull</th>\n",
       "      <th>efermi</th>\n",
       "      <th>elements</th>\n",
       "      <th>final_energy_per_atom</th>\n",
       "      <th>formation_energy_per_atom</th>\n",
       "      <th>pretty_formula</th>\n",
       "      <th>structure</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>mp-1008807</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>{'Rb': 1.0, 'Cu': 1.0, 'O': 1.0}</td>\n",
       "      <td>4.784634</td>\n",
       "      <td>0.996372</td>\n",
       "      <td>1.100617</td>\n",
       "      <td>[Rb, Cu, O]</td>\n",
       "      <td>-3.302762</td>\n",
       "      <td>-0.186408</td>\n",
       "      <td>RbCuO</td>\n",
       "      <td>[[-3.05935361 -3.05935361 -3.05935361] Rb, [0....</td>\n",
       "      <td>57.268924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mp-1009640</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>{'Pr': 1.0, 'N': 1.0}</td>\n",
       "      <td>8.145777</td>\n",
       "      <td>0.759393</td>\n",
       "      <td>5.213442</td>\n",
       "      <td>[Pr, N]</td>\n",
       "      <td>-7.082624</td>\n",
       "      <td>-0.714336</td>\n",
       "      <td>PrN</td>\n",
       "      <td>[[0. 0. 0.] Pr, [1.57925232 1.57925232 1.58276...</td>\n",
       "      <td>31.579717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mp-1016825</td>\n",
       "      <td>0.7745</td>\n",
       "      <td>{'Hf': 1.0, 'Mg': 1.0, 'O': 3.0}</td>\n",
       "      <td>6.165888</td>\n",
       "      <td>0.589550</td>\n",
       "      <td>2.424570</td>\n",
       "      <td>[Hf, Mg, O]</td>\n",
       "      <td>-7.911723</td>\n",
       "      <td>-3.060060</td>\n",
       "      <td>HfMgO3</td>\n",
       "      <td>[[2.03622802 2.03622802 2.03622802] Hf, [0. 0....</td>\n",
       "      <td>67.541269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            band_gap                       composition   density  \\\n",
       "mp-1008807    0.0000  {'Rb': 1.0, 'Cu': 1.0, 'O': 1.0}  4.784634   \n",
       "mp-1009640    0.0000             {'Pr': 1.0, 'N': 1.0}  8.145777   \n",
       "mp-1016825    0.7745  {'Hf': 1.0, 'Mg': 1.0, 'O': 3.0}  6.165888   \n",
       "\n",
       "            e_above_hull    efermi     elements  final_energy_per_atom  \\\n",
       "mp-1008807      0.996372  1.100617  [Rb, Cu, O]              -3.302762   \n",
       "mp-1009640      0.759393  5.213442      [Pr, N]              -7.082624   \n",
       "mp-1016825      0.589550  2.424570  [Hf, Mg, O]              -7.911723   \n",
       "\n",
       "            formation_energy_per_atom pretty_formula  \\\n",
       "mp-1008807                  -0.186408          RbCuO   \n",
       "mp-1009640                  -0.714336            PrN   \n",
       "mp-1016825                  -3.060060         HfMgO3   \n",
       "\n",
       "                                                    structure     volume  \n",
       "mp-1008807  [[-3.05935361 -3.05935361 -3.05935361] Rb, [0....  57.268924  \n",
       "mp-1009640  [[0. 0. 0.] Pr, [1.57925232 1.57925232 1.58276...  31.579717  \n",
       "mp-1016825  [[2.03622802 2.03622802 2.03622802] Hf, [0. 0....  67.541269  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if you have not have the samples data\n",
    "# preset.build('mp_samples', api_key=<your materials project api key>)\n",
    "\n",
    "from xenonpy.datatools import preset\n",
    "\n",
    "data = preset.mp_samples\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In case of the system did not automatically download some descriptor data in XenonPy, please run the following code to sync the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fetching dataset `elements` from https://github.com/yoshida-lab/dataset/releases/download/v0.1.3/elements.pd.xz.\n",
      "fetching dataset `elements_completed` from https://github.com/yoshida-lab/dataset/releases/download/v0.1.3/elements_completed.pd.xz.\n"
     ]
    }
   ],
   "source": [
    "from xenonpy.datatools import preset\n",
    "preset.sync('elements')\n",
    "preset.sync('elements_completed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ave:atomic_number</th>\n",
       "      <th>ave:atomic_radius</th>\n",
       "      <th>ave:atomic_radius_rahm</th>\n",
       "      <th>ave:atomic_volume</th>\n",
       "      <th>ave:atomic_weight</th>\n",
       "      <th>ave:boiling_point</th>\n",
       "      <th>ave:bulk_modulus</th>\n",
       "      <th>ave:c6_gb</th>\n",
       "      <th>ave:covalent_radius_cordero</th>\n",
       "      <th>ave:covalent_radius_pyykko</th>\n",
       "      <th>...</th>\n",
       "      <th>min:num_s_valence</th>\n",
       "      <th>min:period</th>\n",
       "      <th>min:specific_heat</th>\n",
       "      <th>min:thermal_conductivity</th>\n",
       "      <th>min:vdw_radius</th>\n",
       "      <th>min:vdw_radius_alvarez</th>\n",
       "      <th>min:vdw_radius_mm3</th>\n",
       "      <th>min:vdw_radius_uff</th>\n",
       "      <th>min:sound_velocity</th>\n",
       "      <th>min:Polarizability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>mp-1008807</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>174.067140</td>\n",
       "      <td>209.333333</td>\n",
       "      <td>25.666667</td>\n",
       "      <td>55.004267</td>\n",
       "      <td>1297.063333</td>\n",
       "      <td>72.868680</td>\n",
       "      <td>1646.90</td>\n",
       "      <td>139.333333</td>\n",
       "      <td>128.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.360</td>\n",
       "      <td>0.02658</td>\n",
       "      <td>152.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>349.5</td>\n",
       "      <td>317.5</td>\n",
       "      <td>0.802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mp-1009640</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>232.500000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>77.457330</td>\n",
       "      <td>1931.200000</td>\n",
       "      <td>43.182441</td>\n",
       "      <td>1892.85</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>123.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.192</td>\n",
       "      <td>0.02583</td>\n",
       "      <td>155.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>360.6</td>\n",
       "      <td>333.6</td>\n",
       "      <td>1.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mp-1016825</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>153.120852</td>\n",
       "      <td>203.400000</td>\n",
       "      <td>13.920000</td>\n",
       "      <td>50.158400</td>\n",
       "      <td>1420.714000</td>\n",
       "      <td>76.663625</td>\n",
       "      <td>343.82</td>\n",
       "      <td>102.800000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.02658</td>\n",
       "      <td>152.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>302.1</td>\n",
       "      <td>317.5</td>\n",
       "      <td>0.802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 290 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ave:atomic_number  ave:atomic_radius  ave:atomic_radius_rahm  \\\n",
       "mp-1008807          24.666667         174.067140              209.333333   \n",
       "mp-1009640          33.000000         137.000000              232.500000   \n",
       "mp-1016825          21.600000         153.120852              203.400000   \n",
       "\n",
       "            ave:atomic_volume  ave:atomic_weight  ave:boiling_point  \\\n",
       "mp-1008807          25.666667          55.004267        1297.063333   \n",
       "mp-1009640          19.050000          77.457330        1931.200000   \n",
       "mp-1016825          13.920000          50.158400        1420.714000   \n",
       "\n",
       "            ave:bulk_modulus  ave:c6_gb  ave:covalent_radius_cordero  \\\n",
       "mp-1008807         72.868680    1646.90                   139.333333   \n",
       "mp-1009640         43.182441    1892.85                   137.000000   \n",
       "mp-1016825         76.663625     343.82                   102.800000   \n",
       "\n",
       "            ave:covalent_radius_pyykko  ...  min:num_s_valence  min:period  \\\n",
       "mp-1008807                  128.333333  ...                1.0         2.0   \n",
       "mp-1009640                  123.500000  ...                2.0         2.0   \n",
       "mp-1016825                   96.000000  ...                2.0         2.0   \n",
       "\n",
       "            min:specific_heat  min:thermal_conductivity  min:vdw_radius  \\\n",
       "mp-1008807              0.360                   0.02658           152.0   \n",
       "mp-1009640              0.192                   0.02583           155.0   \n",
       "mp-1016825              0.146                   0.02658           152.0   \n",
       "\n",
       "            min:vdw_radius_alvarez  min:vdw_radius_mm3  min:vdw_radius_uff  \\\n",
       "mp-1008807                   150.0               182.0               349.5   \n",
       "mp-1009640                   166.0               193.0               360.6   \n",
       "mp-1016825                   150.0               182.0               302.1   \n",
       "\n",
       "            min:sound_velocity  min:Polarizability  \n",
       "mp-1008807               317.5               0.802  \n",
       "mp-1009640               333.6               1.100  \n",
       "mp-1016825               317.5               0.802  \n",
       "\n",
       "[3 rows x 290 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>mp-1008807</td>\n",
       "      <td>57.268924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mp-1009640</td>\n",
       "      <td>31.579717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mp-1016825</td>\n",
       "      <td>67.541269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               volume\n",
       "mp-1008807  57.268924\n",
       "mp-1009640  31.579717\n",
       "mp-1016825  67.541269"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop = data[data.volume <= 2500]['volume'].to_frame()  # reshape to 2-D\n",
    "desc = Compositions(featurizers='classic').transform(data.loc[prop.index]['composition'])\n",
    "\n",
    "desc.head(3)\n",
    "prop.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xenonpy.datatools import preset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the `xenonpy.datatools.Splitter` to split data into training and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mSplitter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtest_size\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mk_fold\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIterable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mrandom_state\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mshuffle\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m      Data splitter for train and test\n",
       "\u001b[0;31mInit docstring:\u001b[0m\n",
       "Parameters\n",
       "----------\n",
       "size\n",
       "    Total sample size.\n",
       "    All data must have same length of their first dim,\n",
       "test_size\n",
       "    If float, should be between ``0.0`` and ``1.0`` and represent the proportion\n",
       "    of the dataset to include in the test split. If int, represents the\n",
       "    absolute number of test samples. Can be ``0`` if cv is ``None``.\n",
       "    In this case, :meth:`~Splitter.cv` will yield a tuple only contains ``training`` and ``validation``\n",
       "    on each step. By default, the value is set to 0.2.\n",
       "k_fold\n",
       "    Number of k-folds.\n",
       "    If ``int``, Must be at least 2.\n",
       "    If ``Iterable``, it should provide label for each element which will be used for group cv.\n",
       "    In this case, the input of :meth:`~Splitter.cv` must be a :class:`pandas.DataFrame` object.\n",
       "    Default value is None to specify no cv.\n",
       "random_state\n",
       "    If int, random_state is the seed used by the random number generator;\n",
       "    Default is None.\n",
       "shuffle\n",
       "    Whether or not to shuffle the data before splitting.\n",
       "\u001b[0;31mFile:\u001b[0m           ~/projects/XenonPy/xenonpy/datatools/splitter.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Splitter?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(738, 290)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(738, 1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(185, 290)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(185, 1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp = Splitter(prop.shape[0])\n",
    "x_train, x_val, y_train, y_val = sp.split(desc, prop)\n",
    "\n",
    "x_train.shape\n",
    "y_train.shape\n",
    "x_val.shape\n",
    "y_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### model training\n",
    "\n",
    "Model training in pytorch is very flexible.\n",
    "The [official document](https://pytorch.org/tutorials/beginner/pytorch_with_examples.html) explains the concept with examples.\n",
    "In short, training a neuron network model includes:\n",
    "1. calculating loss of training data for the model.\n",
    "2. executing the backpropagation to update the weights between each neuron.\n",
    "3. looping over step 1 and 2 until convergence.\n",
    "\n",
    "In step 1, a calculator, often called `loss function`, is needed to calculate the loss.\n",
    "In step 2, an optimizer will be used.\n",
    "`xenonpy.model.training.Trainer` covers step 3.\n",
    "\n",
    "Here is how you will do it in XenonPy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SequentialLinear(\n",
       "  (layer_0): LinearLayer(\n",
       "    (linear): Linear(in_features=290, out_features=232, bias=True)\n",
       "    (dropout): Dropout(p=0.1)\n",
       "    (normalizer): BatchNorm1d(232, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (activation): ReLU()\n",
       "  )\n",
       "  (layer_1): LinearLayer(\n",
       "    (linear): Linear(in_features=232, out_features=174, bias=True)\n",
       "    (dropout): Dropout(p=0.1)\n",
       "    (normalizer): BatchNorm1d(174, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (activation): ReLU()\n",
       "  )\n",
       "  (layer_2): LinearLayer(\n",
       "    (linear): Linear(in_features=174, out_features=116, bias=True)\n",
       "    (dropout): Dropout(p=0.1)\n",
       "    (normalizer): BatchNorm1d(116, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (activation): ReLU()\n",
       "  )\n",
       "  (layer_3): LinearLayer(\n",
       "    (linear): Linear(in_features=116, out_features=58, bias=True)\n",
       "    (dropout): Dropout(p=0.1)\n",
       "    (normalizer): BatchNorm1d(58, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (activation): ReLU()\n",
       "  )\n",
       "  (output): Linear(in_features=58, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SequentialLinear(290, 1, h_neurons=(0.8, 0.6, 0.4, 0.2))\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Trainer(clip_grad=None, cuda=None, epochs=200, loss_func=MSELoss(),\n",
       "        lr_scheduler=None,\n",
       "        model=SequentialLinear(\n",
       "  (layer_0): LinearLayer(\n",
       "    (linear): Linear(in_features=290, out_features=232, bias=True)\n",
       "    (dropout): Dropout(p=0.1)\n",
       "    (normalizer): BatchNorm1d(232, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (activation): ReLU()\n",
       "  )\n",
       "  (layer_1): LinearLayer(\n",
       "    (linear): Li...\n",
       "    (linear): Linear(in_features=116, out_features=58, bias=True)\n",
       "    (dropout): Dropout(p=0.1)\n",
       "    (normalizer): BatchNorm1d(58, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (activation): ReLU()\n",
       "  )\n",
       "  (output): Linear(in_features=58, out_features=1, bias=True)\n",
       "),\n",
       "        non_blocking=False,\n",
       "        optimizer=Adam (\n",
       "Parameter Group 0\n",
       "    amsgrad: False\n",
       "    betas: (0.9, 0.999)\n",
       "    eps: 1e-08\n",
       "    lr: 0.01\n",
       "    weight_decay: 0\n",
       "))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    optimizer=Adam(lr=0.01),\n",
    "    loss_func=MSELoss()\n",
    ")\n",
    "trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: 100%|██████████| 400/400 [00:06<00:00, 60.86it/s]\n"
     ]
    }
   ],
   "source": [
    "trainer.fit(\n",
    "    x_train=torch.tensor(x_train.values, dtype=torch.float),\n",
    "    y_train=torch.tensor(y_train.values, dtype=torch.float),\n",
    "    epochs=400\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_iters</th>\n",
       "      <th>i_epoch</th>\n",
       "      <th>i_batch</th>\n",
       "      <th>train_mse_loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>397</td>\n",
       "      <td>397</td>\n",
       "      <td>398</td>\n",
       "      <td>1</td>\n",
       "      <td>3684.717041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>398</td>\n",
       "      <td>398</td>\n",
       "      <td>399</td>\n",
       "      <td>1</td>\n",
       "      <td>3058.761719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>3039.683350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     total_iters  i_epoch  i_batch  train_mse_loss\n",
       "397          397      398        1     3684.717041\n",
       "398          398      399        1     3058.761719\n",
       "399          399      400        1     3039.683350"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2ce256d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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    {
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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(figsize=(10, 5), dpi=100)\n",
    "trainer.training_info.tail(3)\n",
    "trainer.training_info.plot(y=['train_mse_loss'], ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing directory and/or file name information!\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = trainer.predict(x_in=torch.tensor(x_val.values, dtype=torch.float)).detach().numpy().flatten()\n",
    "y_true = y_val.values.flatten()\n",
    "\n",
    "y_fit_pred = trainer.predict(x_in=torch.tensor(x_train.values, dtype=torch.float)).detach().numpy().flatten()\n",
    "y_fit_true = y_train.values.flatten()\n",
    "\n",
    "draw(y_true, y_pred, y_fit_true, y_fit_pred, prop_name='Volume ($\\AA^3$)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that although `trainer` can keep training info automatically for us, we still need to convert `DataFrame` to `torch.Tensor` and convert dtype from `np.double` to `torch.float` during training, and eventually convert them back during prediction ourselves. Also, overfitting could be observed in the **prediction vs observation** plot. One possible caused is using all training data in each epoch of the training process. We can use a technique called mini-batch to avoid overfitting, but that will require more coding.\n",
    "\n",
    "To skip these tedious coding steps, we provide an extension system."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we use `ArrayDataset` to wrap our data, and use `torch.utils.data.DataLoader` to a build mini-batch loader."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xenonpy.model.training.dataset import ArrayDataset\n",
    "from torch.utils.data import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = DataLoader(ArrayDataset(x_train, y_train), shuffle=True, batch_size=100)\n",
    "val_dataset = DataLoader(ArrayDataset(x_val, y_val), batch_size=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second, we use `TensorConverter` extension to automatically convert data between `numpy` and `torch.Tensor`.\n",
    "Additionally, we want to trace some stopping criterion and use early stopping when these criterion stop improving.\n",
    "This can be done by using `Validator`.\n",
    "\n",
    "At last, to save our model for latter use, just add `Persist` to the trainer and saving will be done in slient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xenonpy.model.training.extension import Validator, TensorConverter, Persist\n",
    "from xenonpy.model.training.dataset import ArrayDataset\n",
    "from xenonpy.model.utils import regression_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use `trainer.extend` method to stack up extensions. Note that extensions will be executed in the order of insertion, so `TensorConverter` should always go first and `Persist` be the last."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = Trainer(\n",
    "    optimizer=Adam(lr=0.01),\n",
    "    loss_func=MSELoss(),\n",
    ").extend(\n",
    "    TensorConverter(),\n",
    "    Validator(metrics_func=regression_metrics, early_stopping=30, trace_order=5, mae=0.0, pearsonr=1.0),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mPersist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mpath\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpathlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'.'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmodel_class\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mCallable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmodel_params\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m<\u001b[0m\u001b[0mbuilt\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;32min\u001b[0m \u001b[0mfunction\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mincrement\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msync_training_step\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m      Trainer extension for data persistence\n",
       "\u001b[0;31mInit docstring:\u001b[0m\n",
       "Parameters\n",
       "----------\n",
       "path\n",
       "    Path for model saving.\n",
       "model_class\n",
       "    A factory function for model reconstructing.\n",
       "    In most case this is the model class inherits from :class:`torch.nn.Module`\n",
       "model_params\n",
       "    The parameters for model reconstructing.\n",
       "    This can be anything but in general this is a dict which can be used as kwargs parameters.\n",
       "increment\n",
       "    If ``True``, dir name of path will be decorated with a auto increment number,\n",
       "    e.g. use ``model_dir@1`` for ``model_dir``.\n",
       "sync_training_step\n",
       "    If ``True``, will save ``trainer.training_info`` at each iteration.\n",
       "    Default is ``False``, only save ``trainer.training_info`` at each epoch.\n",
       "describe:\n",
       "    Any other information to describe this model.\n",
       "    These information will be saved under model dir by name ``describe.pkl.z``.\n",
       "\u001b[0;31mFile:\u001b[0m           ~/projects/XenonPy/xenonpy/model/training/extension/persist.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Persist?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Persist` need a path to save model. For convenience, we prepare the path name by concatenating the number of neurons in a model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_name(model):\n",
    "    name = []\n",
    "    for n, m in model.named_children():\n",
    "        if 'layer_' in n:\n",
    "            name.append(str(m.linear.in_features))\n",
    "        else:\n",
    "            name.append(str(m.in_features))\n",
    "            name.append(str(m.out_features))\n",
    "    return '-'.join(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SequentialLinear(\n",
       "  (layer_0): LinearLayer(\n",
       "    (linear): Linear(in_features=290, out_features=232, bias=True)\n",
       "    (dropout): Dropout(p=0.1)\n",
       "    (normalizer): BatchNorm1d(232, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (activation): ReLU()\n",
       "  )\n",
       "  (layer_1): LinearLayer(\n",
       "    (linear): Linear(in_features=232, out_features=174, bias=True)\n",
       "    (dropout): Dropout(p=0.1)\n",
       "    (normalizer): BatchNorm1d(174, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (activation): ReLU()\n",
       "  )\n",
       "  (layer_2): LinearLayer(\n",
       "    (linear): Linear(in_features=174, out_features=116, bias=True)\n",
       "    (dropout): Dropout(p=0.1)\n",
       "    (normalizer): BatchNorm1d(116, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (activation): ReLU()\n",
       "  )\n",
       "  (layer_3): LinearLayer(\n",
       "    (linear): Linear(in_features=116, out_features=58, bias=True)\n",
       "    (dropout): Dropout(p=0.1)\n",
       "    (normalizer): BatchNorm1d(58, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (activation): ReLU()\n",
       "  )\n",
       "  (output): Linear(in_features=58, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:  12%|█▏        | 47/400 [00:15<02:06,  2.78it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 31 iterations, finish training at iteration 372\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "model = SequentialLinear(290, 1, h_neurons=(0.8, 0.6, 0.4, 0.2))\n",
    "model\n",
    "\n",
    "model_name = make_name(model)\n",
    "persist = Persist(\n",
    "    f'trained_models/{model_name}', \n",
    "    # -^- required -^-\n",
    "\n",
    "    # -v- optional -v-\n",
    "    increment=False, \n",
    "    sync_training_step=True,\n",
    "    author='Chang Liu',\n",
    "    email='liu.chang.1865@gmail.com',\n",
    "    dataset='materials project',\n",
    ")\n",
    "_ = trainer.extend(persist)\n",
    "trainer.reset(to=model)\n",
    "\n",
    "trainer.fit(training_dataset=train_dataset, validation_dataset=val_dataset, epochs=400)\n",
    "persist(splitter=sp, data_indices=prop.index.tolist())  # <-- calling of this method only after the model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2d100e10>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x750 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(figsize=(10, 5), dpi=150)\n",
    "trainer.training_info.plot(y=['train_mse_loss', 'val_mse'], ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing directory and/or file name information!\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred, y_true = trainer.predict(dataset=val_dataset, checkpoint='pearsonr_1')\n",
    "y_fit_pred, y_fit_true = trainer.predict(dataset=train_dataset, checkpoint='pearsonr_1')\n",
    "draw(y_true, y_pred, y_fit_true, y_fit_pred, prop_name='Volume ($\\AA^3$)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combin random model generating and training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = ParameterGenerator(\n",
    "    in_features=290,\n",
    "    out_features=1,\n",
    "    h_neurons=dict(\n",
    "        data=lambda n: sorted(np.random.uniform(0.2, 0.8, size=n), reverse=True), \n",
    "        repeat=(3, 4)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:   0%|          | 0/400 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SequentialLinear(\n",
      "  (layer_0): LinearLayer(\n",
      "    (linear): Linear(in_features=290, out_features=195, bias=True)\n",
      "    (dropout): Dropout(p=0.1)\n",
      "    (normalizer): BatchNorm1d(195, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (activation): ReLU()\n",
      "  )\n",
      "  (layer_1): LinearLayer(\n",
      "    (linear): Linear(in_features=195, out_features=141, bias=True)\n",
      "    (dropout): Dropout(p=0.1)\n",
      "    (normalizer): BatchNorm1d(141, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (activation): ReLU()\n",
      "  )\n",
      "  (layer_2): LinearLayer(\n",
      "    (linear): Linear(in_features=141, out_features=131, bias=True)\n",
      "    (dropout): Dropout(p=0.1)\n",
      "    (normalizer): BatchNorm1d(131, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (activation): ReLU()\n",
      "  )\n",
      "  (layer_3): LinearLayer(\n",
      "    (linear): Linear(in_features=131, out_features=61, bias=True)\n",
      "    (dropout): Dropout(p=0.1)\n",
      "    (normalizer): BatchNorm1d(61, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (activation): ReLU()\n",
      "  )\n",
      "  (output): Linear(in_features=61, out_features=1, bias=True)\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:  12%|█▏        | 48/400 [00:15<02:06,  2.79it/s]\n",
      "Training:   0%|          | 0/400 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 31 iterations, finish training at iteration 380\n",
      "SequentialLinear(\n",
      "  (layer_0): LinearLayer(\n",
      "    (linear): Linear(in_features=290, out_features=214, bias=True)\n",
      "    (dropout): Dropout(p=0.1)\n",
      "    (normalizer): BatchNorm1d(214, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (activation): ReLU()\n",
      "  )\n",
      "  (layer_1): LinearLayer(\n",
      "    (linear): Linear(in_features=214, out_features=181, bias=True)\n",
      "    (dropout): Dropout(p=0.1)\n",
      "    (normalizer): BatchNorm1d(181, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (activation): ReLU()\n",
      "  )\n",
      "  (layer_2): LinearLayer(\n",
      "    (linear): Linear(in_features=181, out_features=141, bias=True)\n",
      "    (dropout): Dropout(p=0.1)\n",
      "    (normalizer): BatchNorm1d(141, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (activation): ReLU()\n",
      "  )\n",
      "  (layer_3): LinearLayer(\n",
      "    (linear): Linear(in_features=141, out_features=130, bias=True)\n",
      "    (dropout): Dropout(p=0.1)\n",
      "    (normalizer): BatchNorm1d(130, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (activation): ReLU()\n",
      "  )\n",
      "  (output): Linear(in_features=130, out_features=1, bias=True)\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:  10%|█         | 42/400 [00:14<02:18,  2.59it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 31 iterations, finish training at iteration 333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for paras, model in generator(num=2, factory=SequentialLinear):\n",
    "    print(model)\n",
    "    model_name = make_name(model)\n",
    "    persist = Persist(\n",
    "        f'trained_models/{model_name}', \n",
    "        # -^- required -^-\n",
    "        \n",
    "        # -v- optional -v-\n",
    "        increment=False, \n",
    "        sync_training_step=True,\n",
    "        model_class=SequentialLinear,\n",
    "        model_params=paras,\n",
    "        author='Chang Liu',\n",
    "        email='liu.chang.1865@gmail.com',\n",
    "        dataset='materials project',\n",
    "    )\n",
    "    _ = trainer.extend(persist)\n",
    "    trainer.reset(to=model)\n",
    "    \n",
    "    trainer.fit(training_dataset=train_dataset, validation_dataset=val_dataset, epochs=400)\n",
    "    persist(splitter=sp, data_indices=prop.index.tolist())  # <-- calling of this method only after the model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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